The morphology of the human brain is exceptionally complex; reflecting a myriad of inextricably intertwined systems of neuronal cell bodies, axons, and other components. Groupings of neural components that share common structural or functional properties comprise the structural and functional neuroanatomic framework of the brain. Characterization of the morphologic properties of the brain and its component parts, as enabled by state-of-the-art magnetic resonance imaging (MRI) is exceptionally well suited to permit a quantitative study of the parameters relevant to the structural and functional makeup of the human brain in vivo. The goal of this grant is to continue to develop tools and methods for the precise quantitative analysis of brain morphology in health and disease, and to disseminate the tools and results of the application of these tools to the neuroscience community as a whole. Specifically, we will 1) extend our previously developed pixel segmentation and morphological quantification methods, continuing our efforts to develop a unified neuroanatomic segmentation framework and transition these tools to clinical applications on a routinely available software platform; 2) continue our previously developed methods to characterize shape and shape change metrics in normal subjects and pathological patient populations; and 3) dissemination of segmentation tools and comparison methods, as well as the results of image segmentation and volumetric analysis to the community as a whole using the World Wide Web.This application continues to take advantage of several unique aspects that distinguishes it from other related work. First, a unified framework for segmentation and classification in support of a neurologically-based anatomic morphology has emerged. Second, this unified framework incorporates the multispectral nature of MRI data. Third, this framework intrinsically includes estimates of the underlying uncertainty associated with the segmentation and classification process, which supports a rational assessment of sensitivity of a given method. Fourth, this approach expands upon traditional "static" image analysis by incorporation of shape-based analysis for anomaly detection. In addition, we have identified a number of clinical application areas which, in addition to fostering enhanced analytic capabilities to studies in these areas, permits us to optimize the operational efficiency of the resulting analysis. Specifically, the segmentation, classification and shape analysis of MRI data in patients with stroke and Huntington's disease, as welt as the appropriate normative populations, provide a vital testbed for the evaluation of the clinical utility of these morphological analysis techniques.